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A framework for multi-robot coverage analysis of large and complex structures

Author

Listed:
  • Penglei Dai

    (University of Technology Sydney)

  • Mahdi Hassan

    (University of Technology Sydney)

  • Xuerong Sun

    (China Merchants Heavy Industry (Jiangsu) Co., Ltd)

  • Ming Zhang

    (China Merchants Heavy Industry (Jiangsu) Co., Ltd)

  • Zhengwei Bian

    (China Merchants Heavy Industry (Jiangsu) Co., Ltd)

  • Dikai Liu

    (University of Technology Sydney)

Abstract

Coverage analysis is essential for many coverage tasks (e.g., robotic grit-blasting, painting, and surface cleaning) performed by Autonomous Industrial Robots (AIRs). Coverage analysis enables (1) the performance evaluation (e.g., coverage rate and operation efficiency) of AIRs for a coverage task, and (2) the configuration design of a multi-AIR system (e.g., decision on the number of AIRs to be used). Multi-AIR coverage analysis of large and complex structures involves addressing various problems. Thus, a framework is presented in this paper that incorporates various modules (e.g., AIR reachability, AIR base placement, collision avoidance, and area partitioning and allocation) for appropriately addressing the associated problems. The modules within the framework provide the flexibility of utilizing different methods and algorithms, depending on the requirements of the target application. The framework is tested and validated by extensive analyses of 10 different scenarios with up to 10 AIRs.

Suggested Citation

  • Penglei Dai & Mahdi Hassan & Xuerong Sun & Ming Zhang & Zhengwei Bian & Dikai Liu, 2022. "A framework for multi-robot coverage analysis of large and complex structures," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1545-1560, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-021-01745-8
    DOI: 10.1007/s10845-021-01745-8
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    References listed on IDEAS

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    1. Lei Guo & Gui-Hua Lin & Jane J. Ye, 2015. "Solving Mathematical Programs with Equilibrium Constraints," Journal of Optimization Theory and Applications, Springer, vol. 166(1), pages 234-256, July.
    2. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    3. Satheeshkumar Veeramani & Sreekumar Muthuswamy & Keerthi Sagar & Matteo Zoppi, 2020. "Artificial intelligence planners for multi-head path planning of SwarmItFIX agents," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 815-832, April.
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